基于改进人工鱼群算法与RBF神经网络的股票价格预测

股票价格受多种因素影响，这对股票价格预测造成了巨大挑战。近年来，机器学习方法被广泛用于股票价格预测的研究中，然而，现有方法存在相对误差较大、时间复杂度高等缺点。对此，提出基于引力搜索的改进人工鱼群算法AFSA＿GS。该算法将引力搜索中计算质量和加速度的策略分别用于调节人工鱼的视野和步长，从而提高了人工鱼群算法在优化过程中的自适应能力；AFSA＿GS算法还优化了RBF神经网络的相关参数，并使用优化后的网络预测股票价格。在多家上市公司股票数据上进行了实验，实验结果表明，相对于传统的优化算法，采用AFSA＿GS算法优化的RBF神经网络，可以获得更好的股票预测性能。

Stock price prediction based on an improved artificialfish swarm algorithm and RBF neural network

Stock prices are affected by many factors, which poses a great challenge to stock index prediction. In recent years, machine learning has been widely used in the research of stock price prediction. However, the existing methods have some disadvantages such as large relative error and high time complexity. An improved artificial fish swarm algorithm based on gravity search (AFSA_GS) is proposed. AFSA_GS applies the gravity search strategy of calculating mass and acceleration to the visual and step size adjustment of artificial fish respectively, so as to improve the adaptive ability of artificial fish swarm algorithm in the optimization process. AFSA_GS is further used to optimize the relevant parameters of RBF neural network, and the optimized network is used to predict the stock price. Experiments were conducted on the stock data of a number of listed companies. The results show that, compared with the traditional optimization algorithm, AFSA_GS algorithm can be used to optimize RBF neural network, which can obtain better stock prediction performance.